"""simple docstring"""


import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class   __UpperCAmelCase		(      __lowerCAmelCase , unittest.TestCase      ):
				A__ :  Union[str, Any]    				=			GPTaTokenizer
				A__ :  Dict    				=			GPTaTokenizerFast
				A__ :  int    				=			True
				A__ :  int    				=			{'''add_prefix_space''': True}
				A__ :  int    				=			False

				def 						_a      ( self ):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase__   =[
										    "l",
										    "o",
										    "w",
										    "e",
										    "r",
										    "s",
										    "t",
										    "i",
										    "d",
										    "n",
										    "\u0120",
										    "\u0120l",
										    "\u0120n",
										    "\u0120lo",
										    "\u0120low",
										    "er",
										    "\u0120lowest",
										    "\u0120newer",
										    "\u0120wider",
										    "<unk>",
										    "<|endoftext|>",
										]
										lowerCamelCase__   =dict(zip(_lowerCamelCase      ,					range(len(_lowerCamelCase ) ) ) )
										lowerCamelCase__   =["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
										lowerCamelCase__   ={"unk_token": "<unk>"}

										lowerCamelCase__   =os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES["vocab_file"] )
										lowerCamelCase__   =os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES["merges_file"] )
										with open(self.vocab_file      ,					"w"      ,					encoding="utf-8" ) as fp:
																fp.write(json.dumps(_lowerCamelCase ) + "\n" )
										with open(self.merges_file      ,					"w"      ,					encoding="utf-8" ) as fp:
																fp.write("\n".join(_lowerCamelCase ) )

				def 						_a      ( self      ,					**_lowerCamelCase ):
										kwargs.update(self.special_tokens_map )
										return GPTaTokenizer.from_pretrained(self.tmpdirname      ,					**_lowerCamelCase )

				def 						_a      ( self      ,					**_lowerCamelCase ):
										kwargs.update(self.special_tokens_map )
										return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,					**_lowerCamelCase )

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   ="lower newer"
										lowerCamelCase__   ="lower newer"
										return input_text, output_text

				def 						_a      ( self ):
										lowerCamelCase__   =GPTaTokenizer(self.vocab_file      ,					self.merges_file      ,					**self.special_tokens_map )
										lowerCamelCase__   ="lower newer"
										lowerCamelCase__   =["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
										lowerCamelCase__   =tokenizer.tokenize(_lowerCamelCase      ,					add_prefix_space=_lowerCamelCase )
										self.assertListEqual(_lowerCamelCase      ,					_lowerCamelCase )

										lowerCamelCase__   =tokens + [tokenizer.unk_token]
										lowerCamelCase__   =[14, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase )      ,					_lowerCamelCase )

				def 						_a      ( self ):
										if not self.test_rust_tokenizer:
																return

										lowerCamelCase__   =self.get_tokenizer()
										lowerCamelCase__   =self.get_rust_tokenizer(add_prefix_space=_lowerCamelCase )

										lowerCamelCase__   ="lower newer"

										# Testing tokenization
										lowerCamelCase__   =tokenizer.tokenize(_lowerCamelCase      ,					add_prefix_space=_lowerCamelCase )
										lowerCamelCase__   =rust_tokenizer.tokenize(_lowerCamelCase )
										self.assertListEqual(_lowerCamelCase      ,					_lowerCamelCase )

										# Testing conversion to ids without special tokens
										lowerCamelCase__   =tokenizer.encode(_lowerCamelCase      ,					add_special_tokens=_lowerCamelCase      ,					add_prefix_space=_lowerCamelCase )
										lowerCamelCase__   =rust_tokenizer.encode(_lowerCamelCase      ,					add_special_tokens=_lowerCamelCase )
										self.assertListEqual(_lowerCamelCase      ,					_lowerCamelCase )

										# Testing conversion to ids with special tokens
										lowerCamelCase__   =self.get_rust_tokenizer(add_prefix_space=_lowerCamelCase )
										lowerCamelCase__   =tokenizer.encode(_lowerCamelCase      ,					add_prefix_space=_lowerCamelCase )
										lowerCamelCase__   =rust_tokenizer.encode(_lowerCamelCase )
										self.assertListEqual(_lowerCamelCase      ,					_lowerCamelCase )

										# Testing the unknown token
										lowerCamelCase__   =tokens + [rust_tokenizer.unk_token]
										lowerCamelCase__   =[14, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_lowerCamelCase )      ,					_lowerCamelCase )

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# It's very difficult to mix/test pretokenization with byte-level
										# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
										pass

				def 						_a      ( self      ,					_lowerCamelCase=15 ):
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
																						lowerCamelCase__   =self.rust_tokenizer_class.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )

																						# Simple input
																						lowerCamelCase__   ="This is a simple input"
																						lowerCamelCase__   =["This is a simple input 1", "This is a simple input 2"]
																						lowerCamelCase__   =("This is a simple input", "This is a pair")
																						lowerCamelCase__   =[
																						    ("This is a simple input 1", "This is a simple input 2"),
																						    ("This is a simple pair 1", "This is a simple pair 2"),
																						]

																						# Simple input tests
																						self.assertRaises(_lowerCamelCase      ,					tokenizer_r.encode      ,					_lowerCamelCase      ,					max_length=_lowerCamelCase      ,					padding="max_length" )

																						# Simple input
																						self.assertRaises(_lowerCamelCase      ,					tokenizer_r.encode_plus      ,					_lowerCamelCase      ,					max_length=_lowerCamelCase      ,					padding="max_length" )

																						# Simple input
																						self.assertRaises(
																						    _lowerCamelCase      ,					tokenizer_r.batch_encode_plus      ,					_lowerCamelCase      ,					max_length=_lowerCamelCase      ,					padding="max_length"      ,					)

																						# Pair input
																						self.assertRaises(_lowerCamelCase      ,					tokenizer_r.encode      ,					_lowerCamelCase      ,					max_length=_lowerCamelCase      ,					padding="max_length" )

																						# Pair input
																						self.assertRaises(_lowerCamelCase      ,					tokenizer_r.encode_plus      ,					_lowerCamelCase      ,					max_length=_lowerCamelCase      ,					padding="max_length" )

																						# Pair input
																						self.assertRaises(
																						    _lowerCamelCase      ,					tokenizer_r.batch_encode_plus      ,					_lowerCamelCase      ,					max_length=_lowerCamelCase      ,					padding="max_length"      ,					)

				def 						_a      ( self ):
										lowerCamelCase__   =GPTaTokenizer.from_pretrained(self.tmpdirname      ,					pad_token="<pad>" )

										# Simple input
										lowerCamelCase__   ="This is a simple input"
										lowerCamelCase__   =["This is a simple input looooooooong", "This is a simple input"]
										lowerCamelCase__   =("This is a simple input", "This is a pair")
										lowerCamelCase__   =[
										    ("This is a simple input loooooong", "This is a simple input"),
										    ("This is a simple pair loooooong", "This is a simple pair"),
										]

										lowerCamelCase__   =tokenizer.pad_token_id

										lowerCamelCase__   =tokenizer(_lowerCamelCase      ,					padding="max_length"      ,					max_length=30      ,					return_tensors="np" )
										lowerCamelCase__   =tokenizer(_lowerCamelCase      ,					padding=_lowerCamelCase      ,					truncate=_lowerCamelCase      ,					return_tensors="np" )
										lowerCamelCase__   =tokenizer(*_lowerCamelCase      ,					padding="max_length"      ,					max_length=60      ,					return_tensors="np" )
										lowerCamelCase__   =tokenizer(_lowerCamelCase      ,					padding=_lowerCamelCase      ,					truncate=_lowerCamelCase      ,					return_tensors="np" )

										# s
										# test single string max_length padding
										self.assertEqual(out_s["input_ids"].shape[-1]      ,					30 )
										self.assertTrue(pad_token_id in out_s["input_ids"] )
										self.assertTrue(0 in out_s["attention_mask"] )

										# s2
										# test automatic padding
										self.assertEqual(out_sa["input_ids"].shape[-1]      ,					33 )
										# long slice doesn't have padding
										self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
										self.assertFalse(0 in out_sa["attention_mask"][0] )
										# short slice does have padding
										self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
										self.assertTrue(0 in out_sa["attention_mask"][1] )

										# p
										# test single pair max_length padding
										self.assertEqual(out_p["input_ids"].shape[-1]      ,					60 )
										self.assertTrue(pad_token_id in out_p["input_ids"] )
										self.assertTrue(0 in out_p["attention_mask"] )

										# p2
										# test automatic padding pair
										self.assertEqual(out_pa["input_ids"].shape[-1]      ,					52 )
										# long slice pair doesn't have padding
										self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
										self.assertFalse(0 in out_pa["attention_mask"][0] )
										# short slice pair does have padding
										self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
										self.assertTrue(0 in out_pa["attention_mask"][1] )

				def 						_a      ( self ):
										lowerCamelCase__   ="$$$"
										lowerCamelCase__   =GPTaTokenizer.from_pretrained(self.tmpdirname      ,					bos_token=_lowerCamelCase      ,					add_bos_token=_lowerCamelCase )

										lowerCamelCase__   ="This is a simple input"
										lowerCamelCase__   =["This is a simple input 1", "This is a simple input 2"]

										lowerCamelCase__   =tokenizer.bos_token_id

										lowerCamelCase__   =tokenizer(_lowerCamelCase )
										lowerCamelCase__   =tokenizer(_lowerCamelCase )

										self.assertEqual(out_s.input_ids[0]      ,					_lowerCamelCase )
										self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )

										lowerCamelCase__   =tokenizer.decode(out_s.input_ids )
										lowerCamelCase__   =tokenizer.batch_decode(out_sa.input_ids )

										self.assertEqual(decode_s.split()[0]      ,					_lowerCamelCase )
										self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )

				def 						_a      ( self ):
										pass






				def 						_a      ( self ):
										# TODO: change to self.get_tokenizers() when the fast version is implemented
										lowerCamelCase__   =[self.get_tokenizer(do_lower_case=_lowerCamelCase      ,					add_bos_token=_lowerCamelCase )]
										for tokenizer in tokenizers:
																with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
																						lowerCamelCase__   ="Encode this."
																						lowerCamelCase__   ="This one too please."
																						lowerCamelCase__   =tokenizer.encode(_lowerCamelCase      ,					add_special_tokens=_lowerCamelCase )
																						encoded_sequence += tokenizer.encode(_lowerCamelCase      ,					add_special_tokens=_lowerCamelCase )
																						lowerCamelCase__   =tokenizer.encode_plus(
																						    _lowerCamelCase      ,					_lowerCamelCase      ,					add_special_tokens=_lowerCamelCase      ,					return_special_tokens_mask=_lowerCamelCase      ,					)
																						lowerCamelCase__   =encoded_sequence_dict["input_ids"]
																						lowerCamelCase__   =encoded_sequence_dict["special_tokens_mask"]
																						self.assertEqual(len(_lowerCamelCase )      ,					len(_lowerCamelCase ) )

																						lowerCamelCase__   =[
																						    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_lowerCamelCase )
																						]
																						lowerCamelCase__   =[x for x in filtered_sequence if x is not None]
																						self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )






@require_tokenizers
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										# More context:
										# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
										# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
										# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

										lowerCamelCase__   =AutoTokenizer.from_pretrained("facebook/opt-350m"      ,					from_slow=_lowerCamelCase )
										lowerCamelCase__   ="A photo of a cat"

										lowerCamelCase__   =tokenizer.encode(
										    _lowerCamelCase      ,					)
										self.assertEqual(_lowerCamelCase      ,					[2, 250, 1345, 9, 10, 4758] )
										tokenizer.save_pretrained("test_opt" )

										lowerCamelCase__   =AutoTokenizer.from_pretrained("./test_opt" )
										lowerCamelCase__   =tokenizer.encode(
										    _lowerCamelCase      ,					)
										self.assertEqual(_lowerCamelCase      ,					[2, 250, 1345, 9, 10, 4758] )

				def 						_a      ( self ):
										lowerCamelCase__   =AutoTokenizer.from_pretrained("facebook/opt-350m"      ,					use_slow=_lowerCamelCase )
										lowerCamelCase__   ="A photo of a cat"

										lowerCamelCase__   =tokenizer.encode(
										    _lowerCamelCase      ,					)
										# Same as above
										self.assertEqual(_lowerCamelCase      ,					[2, 250, 1345, 9, 10, 4758] )






				@unittest.skip("This test is failing because of a bug in the fast tokenizer" )
				def 						_a      ( self ):
										lowerCamelCase__   =AutoTokenizer.from_pretrained("facebook/opt-350m"      ,					from_slow=_lowerCamelCase )

										lowerCamelCase__   ="bos"
										lowerCamelCase__   =tokenizer.get_vocab()["bos"]

										lowerCamelCase__   ="A photo of a cat"
										lowerCamelCase__   =tokenizer.encode(
										    _lowerCamelCase      ,					)
										# We changed the bos token
										self.assertEqual(_lowerCamelCase      ,					[31957, 250, 1345, 9, 10, 4758] )
										tokenizer.save_pretrained("./tok" )
										lowerCamelCase__   =AutoTokenizer.from_pretrained("./tok" )
										self.assertTrue(tokenizer.is_fast )
										lowerCamelCase__   =tokenizer.encode(
										    _lowerCamelCase      ,					)
										self.assertEqual(_lowerCamelCase      ,					[31957, 250, 1345, 9, 10, 4758] )



"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   bool:




						'''simple docstring'''



						return number & 1 == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()


